{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SemlaFlow: Interactive 3D Molecule Generation Demo\n",
    "\n",
    "This notebook provides an interactive demonstration of SemlaFlow, a framework for generating 3D molecular structures using flow matching and equivariant neural networks.\n",
    "\n",
    "## What is SemlaFlow?\n",
    "\n",
    "SemlaFlow combines:\n",
    "- **Flow Matching**: A continuous-time generative modeling approach that learns vector fields by matching trajectories\n",
    "- **Equivariant Attention Networks**: Neural networks that respect the 3D symmetries of molecular structures\n",
    "- **Optimal Transport**: For aligning molecular structures efficiently"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import py3Dmol\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rdkit import Chem\n",
    "from tqdm.notebook import tqdm\n",
    "from IPython.display import display\n",
    "from rdkit.Chem import Draw, AllChem\n",
    "from ipywidgets import interact, fixed\n",
    "\n",
    "if '..' not in sys.path: sys.path.append('..')\n",
    "\n",
    "import semlaflow.scriptutil as util\n",
    "import semlaflow.util.functional as smolF\n",
    "\n",
    "from semlaflow.models.fm import Integrator, MolecularCFM\n",
    "from semlaflow.data.interpolate import GeometricNoiseSampler\n",
    "from semlaflow.models.semla import EquiInvDynamics, SemlaGenerator\n",
    "from semlaflow.util.molrepr import GeometricMolBatch, GeometricMol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Pretrained Model\n",
    "\n",
    "Configure below as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda with BATCH_SIZE=10\n",
      "Model loaded successfully from ../models/300epochs.ckpt\n",
      "Architecture: semla\n",
      "Model dimension: 384\n",
      "Coordinate sets: 64\n",
      "Number of layers: 12\n",
      "Self-conditioning enabled: True\n"
     ]
    }
   ],
   "source": [
    "MODEL_PATH = \"../models/300epochs.ckpt\"\n",
    "DATASET = \"qm9\"\n",
    "\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "BATCH_SIZE = 10\n",
    "print(f\"Using device: {DEVICE} with BATCH_SIZE={BATCH_SIZE}\")\n",
    "\n",
    "def load_model(model_path, vocab):\n",
    "    \"\"\"Load pretrained model from checkpoint\"\"\"\n",
    "    checkpoint = torch.load(model_path, map_location=DEVICE)\n",
    "    hparams = checkpoint[\"hyper_parameters\"]\n",
    "    \n",
    "    # Configure model parameters\n",
    "    hparams[\"compile_model\"] = False\n",
    "    n_bond_types = util.get_n_bond_types(hparams[\"integration-type-strategy\"])\n",
    "    \n",
    "    # Set architecture (default to semla if not specified)\n",
    "    if hparams.get(\"architecture\") is None: hparams[\"architecture\"] = \"semla\"\n",
    "    \n",
    "    # Build generator based on architecture\n",
    "    if hparams[\"architecture\"] == \"semla\":\n",
    "        dynamics = EquiInvDynamics(\n",
    "            hparams[\"d_model\"],\n",
    "            hparams[\"d_message\"],\n",
    "            hparams[\"n_coord_sets\"],\n",
    "            hparams[\"n_layers\"],\n",
    "            n_attn_heads=hparams[\"n_attn_heads\"],\n",
    "            d_message_hidden=hparams[\"d_message_hidden\"],\n",
    "            d_edge=hparams[\"d_edge\"],\n",
    "            self_cond=hparams[\"self_cond\"],\n",
    "            coord_norm=hparams[\"coord_norm\"],\n",
    "        )\n",
    "        generator = SemlaGenerator(\n",
    "            hparams[\"d_model\"],\n",
    "            dynamics,\n",
    "            vocab.size,\n",
    "            hparams[\"n_atom_feats\"],\n",
    "            d_edge=hparams[\"d_edge\"],\n",
    "            n_edge_types=n_bond_types,\n",
    "            self_cond=hparams[\"self_cond\"],\n",
    "            size_emb=hparams[\"size_emb\"],\n",
    "            max_atoms=hparams[\"max_atoms\"]\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(f\"Architecture {hparams['architecture']} not supported in this demo\")\n",
    "    \n",
    "    # Determine mask index if using masking strategy\n",
    "    type_mask_index = (\n",
    "        vocab.indices_from_tokens([\"<MASK>\"])[0] if hparams[\"integration-type-strategy\"] == \"mask\" else None\n",
    "    )\n",
    "    bond_mask_index = None\n",
    "    \n",
    "    # Create integrator\n",
    "    integration_steps = 100\n",
    "    integrator = Integrator(\n",
    "        integration_steps,\n",
    "        type_strategy=hparams[\"integration-type-strategy\"],\n",
    "        bond_strategy=hparams[\"integration-bond-strategy\"],\n",
    "        type_mask_index=type_mask_index,\n",
    "        bond_mask_index=bond_mask_index,\n",
    "        cat_noise_level=1.0,\n",
    "    )\n",
    "    \n",
    "    # Load model from checkpoint\n",
    "    model = MolecularCFM.load_from_checkpoint(\n",
    "        model_path,\n",
    "        map_location=DEVICE,\n",
    "        gen=generator,\n",
    "        vocab=vocab,\n",
    "        integrator=integrator,\n",
    "        type_mask_index=type_mask_index,\n",
    "        bond_mask_index=bond_mask_index,\n",
    "        **hparams,\n",
    "    )\n",
    "    \n",
    "    model = model.to(DEVICE).eval()\n",
    "    return model, hparams\n",
    "\n",
    "vocab = util.build_vocab()\n",
    "model, hparams = load_model(MODEL_PATH, vocab)\n",
    "\n",
    "print(f\"Model loaded successfully from {MODEL_PATH}\")\n",
    "print(f\"Architecture: {hparams['architecture']}\")\n",
    "print(f\"Model dimension: {hparams['d_model']}\")\n",
    "print(f\"Coordinate sets: {hparams['n_coord_sets']}\")\n",
    "print(f\"Number of layers: {hparams['n_layers']}\")\n",
    "print(f\"Self-conditioning enabled: {model.self_condition}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Molecule Generation\n",
    "\n",
    "Let's generate some molecules and visualize them in both 2D and 3D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_prior_batch(model, n_atoms=15, batch_size=1):\n",
    "    \"\"\"Create a batch of prior noise molecules\"\"\"\n",
    "    # Determine coord scale and dimensions\n",
    "    coord_std = util.QM9_COORDS_STD_DEV if DATASET == \"qm9\" else util.GEOM_COORDS_STD_DEV\n",
    "    n_bond_types = util.get_n_bond_types(model.hparams[\"integration-type-strategy\"])\n",
    "    \n",
    "    # Create noise sampler\n",
    "    prior_sampler = GeometricNoiseSampler(\n",
    "        vocab.size,\n",
    "        n_bond_types,\n",
    "        coord_noise=\"gaussian\",\n",
    "        type_noise=\"uniform-sample\",\n",
    "        bond_noise=\"uniform-sample\",\n",
    "        scale_ot=False,\n",
    "        zero_com=True,\n",
    "        type_mask_index=model.type_mask_index,\n",
    "        bond_mask_index=model.bond_mask_index\n",
    "    )\n",
    "    \n",
    "    # Sample batch\n",
    "    batch = prior_sampler.sample_batch([n_atoms] * batch_size)\n",
    "    \n",
    "    # Convert to dictionary format and ensure proper dtypes\n",
    "    data = {\n",
    "        \"coords\": batch.coords.to(DEVICE) / coord_std,\n",
    "        \"atomics\": batch.atomics.to(DEVICE),\n",
    "        \"bonds\": batch.adjacency.to(DEVICE),\n",
    "        \"charges\": batch.charges.to(DEVICE),\n",
    "        \"mask\": batch.mask.long().to(DEVICE)  # Ensure mask is long type, for some reason it breaks if it isn't set to `long`\n",
    "    }\n",
    "    \n",
    "    return data\n",
    "\n",
    "def generate_molecules(model, n_molecules=5, n_atoms=15, steps=100, strategy=\"log\", noise_level=1.0):\n",
    "    \"\"\"Generate molecules using the model\"\"\"\n",
    "    model.integrator.steps = steps\n",
    "    model.integrator.cat_noise_level = noise_level\n",
    "    \n",
    "    # Generate molecules in batches\n",
    "    batch_size = min(n_molecules, BATCH_SIZE)\n",
    "    batches = (n_molecules + batch_size - 1) // batch_size\n",
    "    \n",
    "    all_molecules = []\n",
    "    all_raw_outputs = []\n",
    "    \n",
    "    for _ in tqdm(range(batches)):\n",
    "        # Adjust batch size for last batch\n",
    "        current_batch_size = min(batch_size, n_molecules - len(all_molecules))\n",
    "        if current_batch_size <= 0:\n",
    "            break\n",
    "            \n",
    "        # Create prior noise and generate molecules\n",
    "        batch = create_prior_batch(model, n_atoms=n_atoms, batch_size=current_batch_size)\n",
    "        with torch.no_grad():\n",
    "            output = model._generate(batch, steps, strategy)\n",
    "            molecules = model._generate_mols(output)\n",
    "        \n",
    "        all_molecules.extend(molecules)\n",
    "        all_raw_outputs.append(output)\n",
    "    \n",
    "    # Filter out None molecules\n",
    "    valid_molecules = [mol for mol in all_molecules if mol is not None]\n",
    "    print(f\"Generated {len(valid_molecules)} valid molecules out of {n_molecules} attempts\")\n",
    "    \n",
    "    return valid_molecules, all_raw_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0379d9b0b7084c03a2a7ead3f1f85cce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 9 valid molecules out of 9 attempts\n"
     ]
    }
   ],
   "source": [
    "# Generate molecules\n",
    "n_molecules = 9\n",
    "molecules, raw_outputs = generate_molecules(model, n_molecules=n_molecules, n_atoms=15, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display 2D representations\n",
    "if molecules:\n",
    "    viz_mols = []\n",
    "    for mol in molecules:\n",
    "        mol_copy = Chem.Mol(mol)\n",
    "        mol_copy = Chem.AddHs(mol_copy, addCoords=True)\n",
    "        AllChem.Compute2DCoords(mol_copy)\n",
    "        viz_mols.append(mol_copy)\n",
    "    \n",
    "    # Display in grid\n",
    "    img = Draw.MolsToGridImage(viz_mols, molsPerRow=3, subImgSize=(300, 300))\n",
    "    display(img)\n",
    "else:\n",
    "    print(\"No valid molecules were generated.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D Visualization\n",
    "\n",
    "Now let's visualize these molecules in 3D using py3Dmol:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "630b15b35adf4cff8fd1ea8213678d3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='Molecule:', max=8), Dropdown(description='Style:', optio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_molecule_3d(mol, width=500, height=400, style='stick', surface=False, spin=False):\n",
    "    \"\"\"Visualize a molecule in 3D using py3Dmol\"\"\"\n",
    "    if mol is None:\n",
    "        return \"Invalid molecule\"\n",
    "    \n",
    "    # Prepare molecule - add hydrogens and generate 3D coordinates\n",
    "    mol_copy = Chem.Mol(mol)\n",
    "    mol_copy = Chem.AddHs(mol_copy, addCoords=True)\n",
    "    \n",
    "    # Optimize 3D coordinates if available\n",
    "    try:\n",
    "        AllChem.MMFFOptimizeMolecule(mol_copy)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # Convert to molblock format for py3Dmol\n",
    "    mb = Chem.MolToMolBlock(mol_copy)\n",
    "    \n",
    "    # Create 3D viewer\n",
    "    view = py3Dmol.view(width=width, height=height)\n",
    "    view.addModel(mb, 'mol')\n",
    "    \n",
    "    # Set style based on input parameter\n",
    "    if style == 'stick':\n",
    "        view.setStyle({'stick': {}})\n",
    "    elif style == 'sphere':\n",
    "        view.setStyle({'sphere': {}})\n",
    "    elif style == 'cartoon':\n",
    "        view.setStyle({'cartoon': {}})\n",
    "    elif style == 'ball_and_stick':\n",
    "        view.setStyle({'stick': {}, 'sphere': {'radius': 0.25}})\n",
    "    \n",
    "    # Add surface if requested\n",
    "    if surface: view.addSurface(py3Dmol.VDW, {'opacity': 0.5, 'color': 'white'})\n",
    "    # Set spin if requested\n",
    "    if spin: view.spin(True)\n",
    "    # Center and zoom view\n",
    "    view.zoomTo()\n",
    "    \n",
    "    return view\n",
    "\n",
    "# Create interactive widget for 3D visualization\n",
    "@interact(\n",
    "    molecule_index=widgets.IntSlider(min=0, max=max(0, len(molecules)-1), step=1, value=0, description='Molecule:'),\n",
    "    style=widgets.Dropdown(options=['stick', 'sphere', 'ball_and_stick'], value='stick', description='Style:'),\n",
    "    surface=widgets.Checkbox(value=False, description='Surface'),\n",
    "    spin=widgets.Checkbox(value=False, description='Spin'),\n",
    "    width=widgets.IntSlider(min=300, max=800, step=50, value=500, description='Width:'),\n",
    "    height=widgets.IntSlider(min=300, max=800, step=50, value=400, description='Height:')\n",
    ")\n",
    "def view_molecule(molecule_index, style, surface, spin, width, height):\n",
    "    \"\"\"Interactive function to view molecules in 3D\"\"\"\n",
    "    if not molecules or molecule_index >= len(molecules):\n",
    "        return \"No molecule available\"\n",
    "    \n",
    "    mol = molecules[molecule_index]\n",
    "    view = visualize_molecule_3d(mol, width, height, style, surface, spin)\n",
    "    \n",
    "    # Calculate and display some properties\n",
    "    from rdkit.Chem import Descriptors, QED\n",
    "    try:\n",
    "        props = {\n",
    "            \"Formula\": Chem.rdMolDescriptors.CalcMolFormula(mol),\n",
    "            \"Molecular Weight\": round(Descriptors.MolWt(mol), 2),\n",
    "            \"LogP\": round(Descriptors.MolLogP(mol), 2),\n",
    "            \"H-Bond Donors\": Descriptors.NumHDonors(mol),\n",
    "            \"H-Bond Acceptors\": Descriptors.NumHAcceptors(mol),\n",
    "            \"Rotatable Bonds\": Descriptors.NumRotatableBonds(mol),\n",
    "            \"QED (drug-likeness)\": round(QED.qed(mol), 3)\n",
    "        }\n",
    "        prop_str = \"<br>\".join([f\"<b>{k}:</b> {v}\" for k, v in props.items()])\n",
    "        display(widgets.HTML(f\"<div style='margin-top: 10px;'><h4>Properties:</h4>{prop_str}</div>\"))\n",
    "    except:\n",
    "        display(widgets.HTML(\"<div>Error calculating properties</div>\"))\n",
    "    \n",
    "    return view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flow Trajectory Visualization\n",
    "\n",
    "One of the most interesting aspects of flow matching is the linear interpolation from noise to a final datapoint (valid molecule). Let's visualize this process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating trajectory...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c48ccb81782247108b43a879a7d9b040",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:45] Explicit valence for atom # 6 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:46] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n",
      "[16:53:47] Explicit valence for atom # 0 H, 2, is greater than permitted\n"
     ]
    }
   ],
   "source": [
    "def generate_trajectory(model, n_atoms=15, n_steps=10, strategy=\"log\"):\n",
    "    \"\"\"Generate a trajectory of molecules from noise to final structure\"\"\"\n",
    "    # Create prior noise\n",
    "    prior = create_prior_batch(model, n_atoms=n_atoms, batch_size=1)\n",
    "\n",
    "    # Sample at different time points\n",
    "    trajectory = []\n",
    "    timesteps = []\n",
    "\n",
    "    # Determine time points based on strategy\n",
    "    if strategy == \"linear\":\n",
    "        time_points = np.linspace(0, 1, n_steps+1)[:-1]  # Exclude t=1 (end point)\n",
    "    elif strategy == \"log\":\n",
    "        time_points = (1 - np.geomspace(0.01, 1.0, n_steps+1))[:-1]  # Exclude t=1 (end point)\n",
    "        time_points = time_points[::-1]  # Reverse to go from 0 to 1\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown ODE integration strategy '{strategy}'\")\n",
    "\n",
    "    # Add the end point manually\n",
    "    time_points = list(time_points) + [0.999]\n",
    "\n",
    "    print(\"Generating trajectory...\")\n",
    "    for t in tqdm(time_points):\n",
    "        times = torch.tensor([t], device=DEVICE, dtype=torch.float32) # Same here, for some reason I have to ensure float32, otherwise it breaks\n",
    "        curr = {k: v.clone() for k, v in prior.items()}\n",
    "\n",
    "        with torch.no_grad():\n",
    "            # Create conditioning batch with zeros if we're using self-conditioning\n",
    "            if model.self_condition:\n",
    "                cond_batch = {\n",
    "                    \"coords\": torch.zeros_like(curr[\"coords\"]),\n",
    "                    \"atomics\": torch.zeros_like(curr[\"atomics\"]),\n",
    "                    \"bonds\": torch.zeros_like(curr[\"bonds\"])\n",
    "                }\n",
    "            else:\n",
    "                cond_batch = None\n",
    "\n",
    "            # Use the model's forward method with conditioning\n",
    "            coords, type_logits, bond_logits, charge_logits = model(curr, times, training=False, cond_batch=cond_batch)\n",
    "\n",
    "            type_probs = torch.softmax(type_logits, dim=-1)\n",
    "            bond_probs = torch.softmax(bond_logits, dim=-1)\n",
    "            charge_probs = torch.softmax(charge_logits, dim=-1)\n",
    "            predicted = {\n",
    "                \"coords\": coords,\n",
    "                \"atomics\": type_probs,\n",
    "                \"bonds\": bond_probs,\n",
    "                \"charges\": charge_probs,\n",
    "                \"mask\": curr[\"mask\"]\n",
    "            }\n",
    "\n",
    "        # Create molecule from current state\n",
    "        mol = model._generate_mols(predicted)[0]\n",
    "        trajectory.append(mol)\n",
    "        timesteps.append(t)\n",
    "\n",
    "    return trajectory, timesteps\n",
    "\n",
    "# Generate a trajectory\n",
    "trajectory, timesteps = generate_trajectory(model, n_atoms=15, n_steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "853ffd0b05ca4447bc0a42ab725361aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='Time step:'), Dropdown(description='Style:', options=('s…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create interactive widget to view trajectory\n",
    "@interact(\n",
    "    timestep=widgets.IntSlider(min=0, max=len(trajectory)-1, step=1, value=0, description='Time step:'),\n",
    "    style=widgets.Dropdown(options=['stick', 'sphere', 'ball_and_stick'], value='stick', description='Style:'),\n",
    "    width=fixed(600),\n",
    "    height=fixed(400),\n",
    "    surface=fixed(False),\n",
    "    spin=fixed(False)\n",
    ")\n",
    "def view_trajectory(timestep, style, width, height, surface, spin):\n",
    "    \"\"\"View a specific timestep in the trajectory\"\"\"\n",
    "    if not trajectory or timestep >= len(trajectory):\n",
    "        return \"No trajectory available\"\n",
    "    \n",
    "    mol = trajectory[timestep]\n",
    "    t = timesteps[timestep]\n",
    "    \n",
    "    print(f\"Viewing time t = {t:.3f}\")\n",
    "    \n",
    "    if mol is None: \n",
    "        return \"Invalid Molecule\"\n",
    "    \n",
    "    return visualize_molecule_3d(mol, width, height, style, surface, spin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Side-by-Side Trajectory View\n",
    "\n",
    "Let's visualize multiple timesteps of the trajectory together to see the evolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_trajectory_grid(trajectory, timesteps, n_cols=3):\n",
    "    \"\"\"Display a grid of molecules from the trajectory\"\"\"\n",
    "    if not trajectory:\n",
    "        print(\"No trajectory available\")\n",
    "        return\n",
    "    \n",
    "    # Filter out None molecules\n",
    "    valid_traj = [(mol, t) for mol, t in zip(trajectory, timesteps) if mol is not None]\n",
    "    if not valid_traj:\n",
    "        print(\"No valid molecules in trajectory\")\n",
    "        return\n",
    "    \n",
    "    mols, times = zip(*valid_traj)\n",
    "    \n",
    "    # Prepare molecules for grid visualization\n",
    "    viz_mols = []\n",
    "    for mol in mols:\n",
    "        mol_copy = Chem.Mol(mol)\n",
    "        Chem.SanitizeMol(mol_copy)\n",
    "        mol_copy = Chem.AddHs(mol_copy, addCoords=True)\n",
    "        AllChem.Compute2DCoords(mol_copy)\n",
    "        viz_mols.append(mol_copy)\n",
    "    \n",
    "    # Create labels with timesteps\n",
    "    labels = [f\"t = {t:.3f}\" for t in times]\n",
    "    \n",
    "    # Calculate grid dimensions\n",
    "    n_mols = len(viz_mols)\n",
    "    n_rows = (n_mols + n_cols - 1) // n_cols\n",
    "    \n",
    "    # Display in grid\n",
    "    img = Draw.MolsToGridImage(viz_mols, molsPerRow=n_cols, subImgSize=(300, 300), legends=labels)\n",
    "    display(img)\n",
    "\n",
    "# Display trajectory grid\n",
    "display_trajectory_grid(trajectory, timesteps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Molecule Generation\n",
    "\n",
    "Let's create an interactive interface to generate molecules with different parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36a16c4817d4497b4a32ac616290f32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=4, description='Number:', max=16, min=1), IntSlider(value=15, descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(\n",
    "    n_molecules=widgets.IntSlider(min=1, max=16, step=1, value=4, description='Number:'),\n",
    "    n_atoms=widgets.IntSlider(min=1, max=30, step=1, value=15, description='Atoms:'),\n",
    "    steps=widgets.IntSlider(min=1, max=200, step=10, value=100, description='Steps:'),\n",
    "    strategy=widgets.Dropdown(options=['linear', 'log'], value='log', description='Strategy:'),\n",
    "    noise_level=widgets.FloatSlider(min=0.1, max=2.0, step=0.1, value=1.0, description='Noise:'),\n",
    "    molsPerRow=widgets.IntSlider(min=1, max=8, step=1, value=3, description='Cols:')\n",
    ")\n",
    "def generate_molecules_interactive(n_molecules, n_atoms, steps, strategy, noise_level, molsPerRow):\n",
    "    \"\"\"Interactive function to generate molecules with different parameters\"\"\"\n",
    "    # Generate molecules\n",
    "    molecules, _ = generate_molecules(\n",
    "        model,\n",
    "        n_molecules=n_molecules, \n",
    "        n_atoms=n_atoms, \n",
    "        steps=steps, \n",
    "        strategy=strategy,\n",
    "        noise_level=noise_level\n",
    "    )\n",
    "    \n",
    "    if not molecules:\n",
    "        print(\"No valid molecules were generated.\")\n",
    "        return\n",
    "    \n",
    "    # Prepare molecules for visualization\n",
    "    viz_mols = []\n",
    "    for mol in molecules:\n",
    "        mol_copy = Chem.Mol(mol)\n",
    "        mol_copy = Chem.AddHs(mol_copy, addCoords=True)\n",
    "        AllChem.Compute2DCoords(mol_copy)\n",
    "        viz_mols.append(mol_copy)\n",
    "    \n",
    "    # Display in grid\n",
    "    img = Draw.MolsToGridImage(viz_mols, molsPerRow=molsPerRow, subImgSize=(300, 300))\n",
    "    display(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Property Analysis of Generated Molecules\n",
    "\n",
    "Let's analyze the properties of a larger batch of generated molecules to see the distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_molecules(molecules):\n",
    "    \"\"\"Analyze molecular properties of a batch of molecules\"\"\"\n",
    "    if not molecules:\n",
    "        print(\"No molecules to analyze\")\n",
    "        return\n",
    "        \n",
    "    import pandas as pd\n",
    "    from rdkit.Chem import Descriptors, QED, Lipinski\n",
    "    \n",
    "    # Calculate properties\n",
    "    properties = []\n",
    "\n",
    "    # Filter None mols\n",
    "    mol = [mol for mol in molecules if mol is not None]\n",
    "    \n",
    "    for mol in molecules:\n",
    "        try:\n",
    "            props = {\n",
    "                'MW': Descriptors.MolWt(mol),\n",
    "                'LogP': Descriptors.MolLogP(mol),\n",
    "                'HBA': Descriptors.NumHAcceptors(mol),\n",
    "                'HBD': Descriptors.NumHDonors(mol),\n",
    "                'TPSA': Descriptors.TPSA(mol),\n",
    "                'RotBonds': Descriptors.NumRotatableBonds(mol),\n",
    "                'QED': QED.qed(mol),\n",
    "                'Num Atoms': mol.GetNumAtoms(),\n",
    "                'Num Rings': Chem.rdMolDescriptors.CalcNumRings(mol)\n",
    "            }\n",
    "            properties.append(props)\n",
    "        except:\n",
    "            continue\n",
    "    \n",
    "    # Display statistics\n",
    "    print(f\"Analyzed {len(properties)} valid molecules\")\n",
    "    df = pd.DataFrame(properties)\n",
    "    display(df.describe())\n",
    "    \n",
    "    # Plot distributions\n",
    "    fig, axes = plt.subplots(3, 3, figsize=(15, 12))\n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    for i, col in enumerate(df.columns):\n",
    "        if i >= len(axes):\n",
    "            break\n",
    "        axes[i].hist(df[col], bins=15, alpha=0.7)\n",
    "        axes[i].set_title(col)\n",
    "        axes[i].set_xlabel('Value')\n",
    "        axes[i].set_ylabel('Count')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf06182774764575a84867ec7f554c17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 50 valid molecules out of 50 attempts\n",
      "Analyzed 50 valid molecules\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>HBA</th>\n",
       "      <th>HBD</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>RotBonds</th>\n",
       "      <th>QED</th>\n",
       "      <th>Num Atoms</th>\n",
       "      <th>Num Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>116.50112</td>\n",
       "      <td>-0.339061</td>\n",
       "      <td>2.34000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>44.675800</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.424802</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.87571</td>\n",
       "      <td>1.109097</td>\n",
       "      <td>0.93917</td>\n",
       "      <td>0.895339</td>\n",
       "      <td>21.044367</td>\n",
       "      <td>1.107304</td>\n",
       "      <td>0.068235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.113186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>70.13500</td>\n",
       "      <td>-3.258700</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237517</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>109.12800</td>\n",
       "      <td>-0.993375</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.890000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.379995</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>122.12300</td>\n",
       "      <td>-0.161650</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.115000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427314</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>126.11400</td>\n",
       "      <td>0.303325</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.382500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.466644</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>143.10200</td>\n",
       "      <td>1.896440</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>97.790000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.571258</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MW       LogP       HBA        HBD       TPSA   RotBonds  \\\n",
       "count   50.00000  50.000000  50.00000  50.000000  50.000000  50.000000   \n",
       "mean   116.50112  -0.339061   2.34000   0.880000  44.675800   1.280000   \n",
       "std     12.87571   1.109097   0.93917   0.895339  21.044367   1.107304   \n",
       "min     70.13500  -3.258700   0.00000   0.000000   0.000000   0.000000   \n",
       "25%    109.12800  -0.993375   2.00000   0.000000  31.890000   0.250000   \n",
       "50%    122.12300  -0.161650   2.00000   1.000000  42.115000   1.000000   \n",
       "75%    126.11400   0.303325   3.00000   1.000000  60.382500   2.000000   \n",
       "max    143.10200   1.896440   4.00000   3.000000  97.790000   5.000000   \n",
       "\n",
       "             QED  Num Atoms  Num Rings  \n",
       "count  50.000000       50.0  50.000000  \n",
       "mean    0.424802       15.0   1.840000  \n",
       "std     0.068235        0.0   1.113186  \n",
       "min     0.237517       15.0   0.000000  \n",
       "25%     0.379995       15.0   1.000000  \n",
       "50%     0.427314       15.0   2.000000  \n",
       "75%     0.466644       15.0   2.000000  \n",
       "max     0.571258       15.0   5.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a larger batch of molecules for analysis\n",
    "analysis_molecules, _ = generate_molecules(model, n_molecules=50, n_atoms=15, steps=100)\n",
    "# Note, would be nice to be able to generate $n$ molecules but with different `n_atoms`, but unsure!\n",
    "\n",
    "# Analyze properties\n",
    "molecule_properties = analyze_molecules(analysis_molecules)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Attention Patterns\n",
    "\n",
    "Let's try to visualize the attention patterns to understand how the model \"thinks\" when generating molecules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_attention_weights(model, mol_index=0, layer_index=0):\n",
    "    \"\"\"Extract attention weights from a model for a specific molecule\"\"\"\n",
    "    # This is a simplified approximation as we can't directly access attention weights\n",
    "    # We'll use bond types as a proxy for attention\n",
    "    \n",
    "    if not hasattr(model, '_last_generated_batch'):\n",
    "        # Generate a molecule to get attention patterns\n",
    "        print(\"Generating a molecule to extract attention patterns...\")\n",
    "        prior = create_prior_batch(model, n_atoms=15, batch_size=1)\n",
    "        with torch.no_grad():\n",
    "            output = model._generate(prior, 100, \"log\")\n",
    "        model._last_generated_batch = output\n",
    "    \n",
    "    # Extract bond distributions from the output\n",
    "    bond_dists = model._last_generated_batch[\"bonds\"][mol_index].cpu().numpy()\n",
    "    \n",
    "    # Combine all bond types into a single attention weight\n",
    "    # Use the sum of non-zero bond probabilities\n",
    "    attention = np.sum(bond_dists[:, :, 1:], axis=2)  # Skip the first bond type (no bond)\n",
    "    \n",
    "    return attention\n",
    "\n",
    "def visualize_attention(mol, attention_weights, threshold=0.3, width=600, height=500):\n",
    "    \"\"\"Visualize attention between atoms in a molecule\"\"\"\n",
    "    if mol is None:\n",
    "        return \"Invalid molecule\"\n",
    "    \n",
    "    # Prepare molecule - add hydrogens and generate 3D coordinates\n",
    "    mol_copy = Chem.Mol(mol)\n",
    "    mol_copy = Chem.AddHs(mol_copy, addCoords=True)\n",
    "    \n",
    "    # Optimize 3D coordinates\n",
    "    try:\n",
    "        AllChem.MMFFOptimizeMolecule(mol_copy)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # Get atom positions\n",
    "    conf = mol_copy.GetConformer()\n",
    "    n_atoms = mol_copy.GetNumAtoms()\n",
    "    \n",
    "    # Set up the 3D viewer\n",
    "    view = py3Dmol.view(width=width, height=height)\n",
    "    mb = Chem.MolToMolBlock(mol_copy)\n",
    "    view.addModel(mb, 'mol')\n",
    "    view.setStyle({'stick': {}, 'sphere': {'radius': 0.3}})\n",
    "    \n",
    "    # Limit attention weights to atoms in the molecule\n",
    "    effective_n = min(attention_weights.shape[0], n_atoms)\n",
    "    \n",
    "    # Add cylinders for attention connections\n",
    "    max_weight = np.max(attention_weights)\n",
    "    for i in range(effective_n):\n",
    "        for j in range(i+1, effective_n):\n",
    "            weight = attention_weights[i, j] / max_weight\n",
    "            \n",
    "            if weight > threshold:\n",
    "                atom_i_pos = conf.GetAtomPosition(i)\n",
    "                atom_j_pos = conf.GetAtomPosition(j)\n",
    "                \n",
    "                # Color based on weight (red for strong, blue for weak)\n",
    "                red = min(1.0, weight * 2)\n",
    "                blue = max(0.0, 1.0 - weight * 2)\n",
    "                color = f'rgb({int(red*255)},0,{int(blue*255)})'\n",
    "                view.addCylinder({\n",
    "                    'start': {'x': atom_i_pos.x, 'y': atom_i_pos.y, 'z': atom_i_pos.z},\n",
    "                    'end': {'x': atom_j_pos.x, 'y': atom_j_pos.y, 'z': atom_j_pos.z},\n",
    "                    'radius': weight * 0.15,  # Scale weight for visualization\n",
    "                    'color': color,\n",
    "                    'fromCap': True,\n",
    "                    'toCap': True,\n",
    "                    'opacity': 0.7\n",
    "                })\n",
    "    \n",
    "    view.zoomTo()\n",
    "    return view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d00efaa9bc54c1b9ca767f54b5f7924",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 1 valid molecules out of 1 attempts\n",
      "Generating a molecule to extract attention patterns...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e50c498d4f4342cab1d23f047d3699a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='Threshold:', max=0.9, min=0.1, step=0.05), Output())…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a new molecule to get fresh attention patterns\n",
    "if hasattr(model, '_last_generated_batch'):\n",
    "    del model._last_generated_batch\n",
    "\n",
    "attn_molecules, _ = generate_molecules(model, n_molecules=1, n_atoms=15, steps=100)\n",
    "if attn_molecules:\n",
    "    mol = attn_molecules[0]    \n",
    "    attention = extract_attention_weights(model)\n",
    "    @interact(\n",
    "        threshold=widgets.FloatSlider(min=0.1, max=0.9, step=0.05, value=0.5, description='Threshold:'),\n",
    "        width=fixed(600),\n",
    "        height=fixed(500)\n",
    "    )\n",
    "    def view_attention(threshold, width, height):\n",
    "        \"\"\"View attention patterns with adjustable threshold\"\"\"\n",
    "        return visualize_attention(mol, attention, threshold, width, height)\n",
    "else:\n",
    "    print(\"Failed to generate a molecule for attention visualization\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this notebook, we've explored SemlaFlow's capabilities for 3D molecular generation using flow matching. The key features we've demonstrated include:\n",
    "\n",
    "1. **Basic Molecule Generation**: Using pretrained models to generate valid 3D molecular structures\n",
    "2. **Interactive 3D Visualization**: Exploring generated molecules in 3D using py3Dmol\n",
    "3. **Flow Trajectories**: Visualizing the gradual transformation from noise to molecule\n",
    "4. **Property Analysis**: Analyzing the chemical properties of generated molecules\n",
    "5. **Attention Patterns**: Getting insights into how the model's attention mechanism works\n",
    "\n",
    "\n",
    "SemlaFlow combines equivariant neural networks and flow matching to create a powerful framework for molecular generation that respects the 3D nature of chemical structures."
   ]
  }
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